NO MORE GUESS­ING

Ed­mond, Okla., an af­flu­ent sub­urb north of Ok­la­homa City, has drawn the in­ter­est of ma­jor re­tail­ers. St. Louis-based Mercy health sys­tem has had its eye on Ed­mond, too.

With a pop­u­la­tion of more than 80,000, Ed­mond is younger and wealth­ier than Ok­la­homa City, with a me­dian age of 34.8, me­dian house­hold in­come of $66,535, and a pro­jected five-year growth rate of 11%.

The 35-hos­pi­tal Mercy sys­tem used a va­ri­ety of so­phis­ti­cated data tech­niques to de­ter­mine what ser­vices and spe­cial­ties could be suc­cess­ful there. For in­stance, psy­cho­graphic data from Bux­ton, a Fort Worth, Texas-based cus­tomer an­a­lyt­ics com­pany, showed lo­cal health­care con­sump­tion trends. Ed­mond is “a well-es­tab­lished com­mu­nity, so you’ve got a lit­tle bit of an aging pop­u­la­tion, but you’ve also got some younger folks mov­ing in rais­ing fam­i­lies,” said Nikki Viner, Mercy’s vice pres­i­dent of mar­ket devel­op­ment.

A year ago, Mercy opened the $88 mil­lion Mercy Ed­mond I-35, 10 miles up the in­ter­state from its Ok­la­homa City hos­pi­tal. The fa­cil­ity fea­tures a sports per­for­mance and fit­ness cen­ter, out­pa­tient surgery, di­ag­nos­tic imag­ing, phar­macy and lab­o­ra­tory ser­vices and a con­ve­nient-care cen­ter. It has nearly 40 physi­cians and other providers. In plan­ning a build­ing such as Mercy Ed­mond, “you have to think like a re­tailer,” Viner said.

Health sys­tems like Mercy long have stud­ied de­mo­graphic and mar­ket­ing data from such sources as the U.S. Cen­sus Bureau, health-plan­ning agen­cies and hos­pi­tal as­so­ci­a­tions, in de­cid­ing where to lo­cate new fa­cil­i­ties. In­creas­ingly, they are us­ing new sources of in­for­ma­tion such as elec­tronic health records, health in­sur­ers’ billing records and data used by ma­jor re­tail­ers. Hos­pi­tal mar­ket­ing spe­cial­ists, geo-an­a­lytic gu­rus, health plan­ners and ar­chi­tects mash up the data, over­lay­ing in­for­ma­tion from a va­ri­ety of sources to cre­ate maps to vi­su­al­ize mar­ket op­por­tu­ni­ties.

Re­tail­ers for many years have used geo-an­a­lyt­ics—vi­su­al­iza­tion or map­ping tools in­te­grat­ing so­phis­ti­cated ge­o­graph­i­cal and mar­ket­ing data—to de­cide where to open stores and restau­rants. Health­care is start­ing to go that way, too.

Viner said se­lect­ing health fa­cil­ity lo­ca­tions is just like plan­ning for a re­tail op­er­a­tion, mov­ing be­yond tra­di­tional de­mo­graphic fac­tors such as age, ed­u­ca­tion and in­come to the types of data that a Tar­get or Wal­green store tap. For Mercy, it’s im­por­tant to use all the best avail­able data to make the right choices on where to place ser­vices such as pri­mary care or a spe­cialty hos­pi­tal. “Health­care is be­com­ing more con­sumer-fo­cused and re­tail-ori­ented,” Viner said.

Dr. Este Ger­aghty, chief med­i­cal of­fi­cer for Red­lands, Calif.based Esri, a map­ping tech­nol­ogy com­pany, said health or­ga­ni­za­tions gen­er­ally have sim­i­lar data needs to those of re­tail­ers, in­clud­ing pop­u­la­tion de­mo­graph­ics, pop­u­la­tion den­sity and mar­ket po­ten­tial. But they may make very dif­fer­ent choices based on that data when sit­ing a clinic for low­in­come peo­ple ver­sus pick­ing a spot for a high-end cloth­ing store, for in­stance. “The data are much the same, but the per­spec­tive is go­ing to be to­tally dif­fer­ent,” she said.

Net­work anal­y­sis is es­pe­cially im­por­tant in de­ter­min­ing lo­ca­tions for health­care fa­cil­i­ties, to un­der­stand and over­come bar­ri­ers to ac­cess­ing care. “You can an­a­lyze mul­ti­modal trans­porta­tion time to a clinic—such as by car, bi­cy­cle or po­ten­tially even public trans­porta­tion—dur­ing nor­mal com­pared to peak traf­fic times,” Ger­aghty said. “Such considerations are crit­i­cally im­por­tant if we are se­ri­ous about re­duc­ing dis­par­i­ties in ser­vice ac­cess.”

Bill Stin­neford, se­nior vice pres­i­dent of Bux­ton, said his firm helps health­care or­ga­ni­za­tions iden­tify po­ten­tial new pa­tients by spe­cialty at the house­hold level, look­ing across their op­er­at­ing ar­eas. The firm helps de­ter­mine where to place new fa­cil­i­ties, helps se­lect the right ser­vice lines for each fa­cil­ity and as­sists with im­prov­ing mar­ket­ing cam­paigns. Us­ing Bux­ton’s Web-based SCOUT an­a­lyt­ics plat­form, clients can map mar­ket op­por­tu­ni­ties and ac­cess cus­tom re­ports. Bux­ton’s clients in­clude Florida Hos­pi­tal in Or­lando and the Marsh­field Clinic in Wis­con­sin.

Stin­neford cau­tioned that a good site for a re­tailer might not be good for a health­care fa­cil­ity, which can­not sim­ply pig­gy­back off McDon­ald’s or a Wal-Mart’s mar­ket re­search. “Just be­cause it’s good for them doesn’t mean it’s go­ing to be good for you,” he said.

Health­care or­ga­ni­za­tions need to do their own homework, an­a­lyz­ing their cus­tomers from data ex­tracted from EHRs and health in­sur­ance claims data to see what kinds of ser­vices po­ten­tial health­care cus­tomers use, where they live and what types of cov­er­age they have. Th­ese data can be com­bined with mar­ket data, traf­fic pat­tern in­for­ma­tion and re­tail lo­ca­tion maps.

Anal­y­sis of all th­ese data re­veal “the pat­terns in the chaos” to re­veal the best lo­ca­tions, Stin­neford said. “Maybe they have a dif­fer­ent payer type, or are in a dif­fer­ent life stage, or for what­ever rea­son they just don’t use ur­gent care that much,” he said. “Once you un­der­stand the DNA of what makes a suc­cess­ful lo­ca­tion, you can score what­ever geog­ra­phy you’re look­ing for.”

An­other caveat he of­fers is that it can be mis­lead­ing to use tra­di­tional de­mo­graphic data to come up with av­er­ages. For in­stance, the wealth­i­est neigh­bor­hoods in Hous­ton are within a mile of the poor­est ones, Stin­neford said. “There are dif­fer­ent in­sur­ance types and they’re all mucked to­gether. That’s why you have to get down to the in­di­vid­ual house­hold, and the data are avail­able to look at how many peo­ple are the right kind of peo­ple.”

Hos­pi­tals typ­i­cally have re­lied on in­pa­tient data for plan­ning. But the grow­ing shift to out­pa­tient care has cre­ated a gap in the avail­abil­ity of use­ful data be­cause out­pa­tient fa­cil­i­ties of­ten don’t have to re­port data to state agen­cies. Still, the avail­abil­ity of out­pa­tient data is im­prov­ing as some states and health­care an­a­lyt­ics firms, such as Tru­ven Health An­a­lyt­ics and Sg2, are col­lect­ing and an­a­lyz­ing it, said Don­ald Belle­feuille, a health­care strate­gist in the Bos­ton of­fice of NBBJ, an in­ter­na­tional health ar­chi­tec­ture firm.

He said the em­pha­sis on data anal­y­sis and pop­u­la­tion health man­age­ment in the Af­ford­able Care Act is lead­ing to im­proved data ac­cess to plan ser­vices and fa­cil­i­ties and to help health­care or­ga­ni­za­tions de­cide whether to buy ex­ist­ing med­i­cal prac­tices or build new ones.

Crys­tal Run Health­care, a 350-provider, mul­ti­spe­cialty group prac­tice with 32 sites in the lower Hud­son Val­ley, N.Y., used to rely pri­mar­ily on the ad­dresses of pa­tients served at its main fa­cil­ity in Mid­dle­town, N.Y, to plan new sites. “Es­sen­tially, we took a leap of faith,” said Dr. Gre­gory Spencer, chief med­i­cal and chief med­i­cal in­for­ma­tion of­fi­cer at Crys­tal Run.

But now, us­ing its own data ware­house, ap­pli­ca­tions from Salt Lake City-based Health Cat­a­lyst and geo-an­a­lyt­ics dash­boards de­vel­oped us­ing Tableau, Crys­tal Run uses much richer data to make sit­ing de­ci­sions. Spencer said his or­ga­ni­za­tion now can an­swer such ques­tions as “Who are th­ese peo­ple? What are their con­di­tions? What are their de­mo­graph­ics? Which spe­cial­ists are see­ing them?”

Crys­tal Run ap­plied this data-based ap­proach to plan a $30 mil­lion fa­cil­ity sched­uled to open in July near a Wal-Mart in New­burgh, N.Y., about 20 miles from the group’s main cam­pus in Mid­dle­town. Spencer said the prac­tice de­cided to build the fa­cil­ity af­ter data showed about 20% of its pa­tients came from near New­burgh, pop­u­la­tion 28,866, a de­clin­ing, racially di­verse city with a me­dian in­come of $35,731. The new cen­ter will fea­ture 20 spe­cial­ties, an ur­gent-care cen­ter, women’s imag­ing, di­ag­nos­tic testing and a clin­i­cal lab.

Spencer said the an­a­lyt­ics give his or­ga­ni­za­tion con­fi­dence that the New­burgh fa­cil­ity will reach ca­pac­ity quickly. The days of guess­ing are over. “You have to use all the in­for­ma­tion you can just so you don’t waste money,” he said.

Health­care or­ga­ni­za­tions need to do their own homework, an­a­lyz­ing their cus­tomers from data ex­tracted from elec­tronic health records and health in­sur­ance claims data to see what kinds of ser­vices po­ten­tial health­care cus­tomers use, where they live and what types of have. cov­er­age they

tals and 22 ur­gent-care cen­ters, just opened a tower in Kis­sim­mee, broke ground on a hos­pi­tal in Apopka and is build­ing a large out­pa­tient com­plex with a free­stand­ing emer­gency depart­ment, sur­gi­cen­ter imag­ing cen­ter and of­fice in Win­ter Gar­den.

Ghosn said Florida Hos­pi­tal asks three key ques­tions be­fore de­cid­ing to build: Is there a com­mu­nity need? Does Florida Hos­pi­tal need to ex­pand its net­work in that spe­cific mar­ket? Is that mar­ket pro­jected to grow? “Usu­ally, if the an­swer is yes to all three, it be­comes an at­trac­tive mar­ket we want to en­ter,” he said.

Florida Hos­pi­tal uses state plan­ning data to get a his­toric view of pop­u­la­tion and vol­ume of health ser­vices based on health­care uti­liza­tion and de­mo­graph­ics. Its own data pro­vide a snap­shot of present con­di­tions. And firms such as Tru­ven, Sg2 and the Ad­vi­sory Board of­fer fore­cast­ing mod­els to project fu­ture de­mand.

For ex­am­ple, Florida Hos­pi­tal broke ground this year for the $203 mil­lion Florida Hos­pi­tal Apopka sched­uled to open in 2017 with 120 beds, up from 50 beds in the sys­tem’s cur­rent hos­pi­tal near the city cen­ter. The new hos­pi­tal will of­fer gen­eral surgery, or­tho­pe­dics and ear, nose and throat care.

The fa­cil­ity’s lo­ca­tion will reach out to new com­mu­ni­ties that have de­vel­oped out­side of Apopka in Or­ange County. The city had a pop­u­la­tion of 45,587 in 2013, an in­crease of 9.5% since 2010. Me­dian house­hold in­come is $59,424, nearly $10,000 a year more than the state me­dian. Ghosn said the fa­cil­ity is aimed at meet­ing needs in Apopka as well as ex­pand­ing the sys­tem’s reach into new mar­kets.

David Crock­ett, a se­nior re­search direc­tor at Health Cat­a­lyst, whose clients in­clude Kaiser Per­ma­nente and Stan­ford Hos­pi­tal & Clin­ics, said health­care or­ga­ni­za­tions are tak­ing ad­van­tage of more in­tel­li­gent geo-an­a­lytic re­sources. His com­pany is de­vel­op­ing tools that de­ter­mine the geo­graphic ser­vice area of a health­care sys­tem and high­light char­ac­ter­is­tic dis­ease dis­tri­bu­tion across that pop­u­la­tion. In ad­di­tion, the tools pro­vide a hi­er­ar­chy of care fa­cil­ity lev­els, and in­clude de­tails of pa­tient drive-time ac­cess to needed ser­vices such as phar­ma­cies, clin­ics, hos­pi­tals and re­ha­bil­i­ta­tion cen­ters.

Crock­ett said the trick is to take in­for­ma­tion gen­er­ated by the health­care sys­tem it­self and merge that with public data sets such as cen­sus statis­tics on pop­u­la­tion den­sity and in­come lev­els. Ad­di­tional data on dis­ease rates, crime statis­tics or air qual­ity can be added, along with lo­ca­tions of farm­ers mar­kets, gyms and parks. “The sheer quan­tity of data avail­able to­day al­lows clients to make more in­formed de­ci­sions than in the past, where ed­u­cated guesses were the best way to pick lo­ca­tions and ser­vices,” he said.

Still, Crock­ett said, the health­care in­dus­try is “at least 15 years be­hind the curve” set by the re­tail, lo­gis­tics and en­ergy in­dus­tries. “Health­care just hasn’t rec­og­nized the op­por­tu­nity. But it’s start­ing,” he said.

Florida Hos­pi­tal uses state plan­ning data to get a his­toric view of pop­u­la­tion and vol­ume of health ser­vices based on health­care uti­liza­tion and de­mo­graph­ics.